This work explores capabilities of the pre-trained CLIP vision-language model to identify satellite images affected by clouds. Several approaches to using the model to perform cloud presence detection are proposed and evaluated, including a purely zero-shot operation with text prompts and several fine-tuning approaches. Furthermore, the transferability of the methods across different datasets and sensor types (Sentinel-2 and Landsat-8) is tested. The results that CLIP can achieve non-trivial performance on the cloud presence detection task with apparent capability to generalise across sensing modalities and sensing bands. It is also found that a low-cost fine-tuning stage leads to a strong increase in true negative rate. The results demonstrate that the representations learned by the CLIP model can be useful for satellite image processing tasks involving clouds.
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
China University of Petroleum-Beijing at Karamay（中国石油大学（北京）克拉玛依校区）；Guizhou University（贵州大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Shanghai Jiao Tong University（上海交通大学）
SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting
Mediterranean Agronomic Institute of Montpellier - CIHEAM-IAMM（地中海农业研究院-CIHEAM-IAMM）；Inria（法国国家信息与自动化技术研究院）；INRAE（法国国家农业研究咨询中心）；Cirad（国际热带农业研究中心）；UMR TETIS（TETIS联合研究单位）；Univ. of Montpellier（蒙彼利埃大学）
FusionRS: A Large-Scale RGB-Infrared-Style Remote Sensing Dataset for Cross-Modal Vision-Language Learning
China University of Petroleum-Beijing at Karamay（中国石油大学（北京）克拉玛依校区）；University of Electronic Science and Technology of China（电子科技大学）；Tianjin University（天津大学）
AnchorScore: A CLIP-Based Diagnostic of MLLM Annotation Difficulty
School of Foreign Studies, Changsha University of Science and Technology（长沙理工大学外国语学院）；School of Education, Hunan Agricultural University（湖南农业大学教育学院）；School of Information and Intelligence, Hunan Agricultural University（湖南农业大学信息与智能科学学院）
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment
organization= Orion Lab, National Observatory of Athens \& National Technical University of Athens , country= Greece；organization= Department of Informatics \& Telematics, Harokopio University of Athens , country= Greece；organization= Faculty of Electrical Engineering；organization= BIFOLD - Berlin Institute for the Foundations of Learning
SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data
Istituto di Matematica Applicata e Tecnologie Informatiche ‘E. Magenes’ - CNR（意大利国家研究委员会“E. 马涅斯”应用数学与信息技术研究所）；University of Chile（智利大学）；University of Science, Ho Chi Minh City（胡志明市科学大学）；Viet Nam National University, Ho Chi Minh City（胡志明市越南国家大学）；John von Neumann Institute（约翰·冯·诺依曼研究所）
SEDS: Semantically Enhanced Dual-Stream Encoder for Sign Language Retrieval
University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Merchants Union Consumer Finance Company Limited（招商局消费金融有限公司）
Exploring the Potential of Contrastive Language-Image Pre-training for Multi-Source Remote Sensing Data
School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Space-based Computing System Research Center, Zhejiang Lab（之江实验室天基计算系统研究中心）
Forest canopy height estimation from satellite RGB imagery using large-scale airborne LiDAR-derived training data and monocular depth estimation
Beijing Normal University（北京师范大学）；CSIRO Environment（英联邦科学与工业研究组织环境部）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Wuhan University（武汉大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Learning to See More: UAS-Guided Super-Resolution of Satellite Imagery for Precision Agriculture
Esri New York, NY（Esri纽约分校）；Microsoft Research Redmond, WA（微软研究院雷德蒙德分校）；USDA - Agricultural Research Service University Park, PA（美国农业部农业研究服务局帕克分校）；Dept. of Electrical Engineering and Computer Science Alabama A&M University, AL（电气工程与计算机科学系阿拉巴马-阿马大学）；Dept. of Environmental Science and Technology Univ. of Maryland, College Park, MD（环境科学与技术系马里兰大学学院市分校）
Feature-Augmented Deep Networks for Multiscale Building Segmentation in High-Resolution UAV and Satellite Imagery
Center for Geospatial Research, Department of Geography University of Georgia（地理空间研究中心，地理系，佐治亚大学）；Photogrammetry and Remote Sensing Department Indian Institute of Remote Sensing（摄影测量与遥感系，印度遥感研究所）；SEAI Lab, Department of Geography and the Environment University of Texas at Austin（SEAI实验室，地理与环境系，德克萨斯大学奥斯汀分校）
Buildings Classification using Very High Resolution Satellite Imagery
Lebanese University（黎巴嫩大学）；Islamic University of Lebanon（黎巴嫩伊斯兰大学）；MINES ParisTech（巴黎高科矿业学院）；National Center for Remote Sensing - CNRS（法国国家科学研究中心国家遥感中心）
